generative ai

MANUSAKSI-AI v1.1 A Human-Authenticated Framework for Documenting Human–AI Interaction, Emergent Experience, and Human–AI Lexicon

Generative Artificial Intelligence is increasingly becoming part of human thinking, writing, research, creativity, decision-making, and everyday conversation. This development creates a methodological problem for documenting Human-AI interaction: how can a human experience involving AI be recorded without allowing AI-generated language to become confused with human testimony, observed events, or historical fact? This working paper introduces MANUSAKSI-AI, a human-authenticated framework for documenting Human-AI interaction events, their provenance, interpretation, and emergent terminology. The framework is based on a simple epistemic distinction: AI may generate language; Human authenticates experience. MANUSAKSI-AI identifies the Human as the Human Principal / Human Witness and the AI as an AI Agent / Interpreter. AI may analyze, interpret, hypothesize, organize, and narrate. However, the authority to authenticate whether a lived human experience actually occurred remains with the Human Principal. The framework introduces an evidence hierarchy, provenance architecture, Human Authentication Gate, event-record schema, anti-hallucination rules, and the "(it happened)" principle. The latter is proposed as a provenance marker for narratives grounded in documented Human-AI encounters and validated by the human participant. The paper also proposes the Kamus Manusaksi-AI, a living lexicon documenting vocabulary emerging from Human-AI relations. The first documented term in the present research trajectory is "Manusaksi-AI", a neologistic formation derived from manusia (human), saksi (witness), and AI. Its conceptual formulation emerged through a documented Human-AI conversation on 29 August 2026. Version 1.1 Update Note: This version introduces formal academic compliance updates, including the inclusion of a comprehensive reference list for the intellectual lenses mentioned in the framework, and the addition of specific ethical, funding, and conflict-of-interest declarations required for journal submission and public release. This Version 1.1 is released as an evolving research artifact. It is intended for documentation, replication, critique, refinement, and subsequent empirical testing rather than as a finalized scientific standard.

AI Shepherds and Electric Sheep: Leading and Teaching in the Age of Artificial Intelligence

The theology chapters may be the most valuable in the book for a broad audience that spans pastors, church leaders, and lay people who may or may not regularly work with AI, and will help those teaching and preaching to connect doctrine to current and emerging AI content and methodology.

Efficiency, but at what cost? Perspectives on generative AI uses, benefits, and drawbacks from social change communicators

From Days to Hours: Artificial Intelligence in Antimicrobial Resistance Diagnostics and Drug Discovery, and Why No Tool Has Yet Reached the Clinic

Artificial intelligence has moved convincingly beyond proof-of-concept in AMR diagnostics and discovery, but its path to the clinic now depends less on further algorithmic refinement than on prospective validation, equitable data representation, and interpretability standards that clinicians can reasonably trust.

Ethical Dilemmas of Generative AI in Education and Their Implications for SDG 4: A Systematic Review from a Global and Inclusive Perspective

The findings reveal that current discussions on GAI in education are largely characterized by retrospective and crisis-driven ethical framings, predominantly focused on risks such as plagiarism, bias, and misinformation, while offering limited engagement with proactive, context-sensitive strategies aligned with SDG 4.

Integrating Large Language Models (LLMs) with Oracle 26AI for Advanced Enterprise Analytics and Knowledge Management

Results indicate that LLM-augmented analytics on a converged Oracle 26AI platform can reduce average analytical query resolution time by approximately 60 percent relative to traditional BI report cycles, achieve semantic retrieval precision above 90 percent for enterprise knowledge corpora, and reduce generative output hallucination rates by more than half when grounding is enforced through in-database retrieval.

Generative AI in Higher Education: The Good, the Bad, and the Ugly

Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette‐Based Study

While artificial intelligence produced more comprehensive information, it generated clinically unsafe content and scored significantly lower than nurses in empathy and readability, and healthcare organizations should establish protocols requiring nurse verification of all artificial intelligence-generated discharge content.

Comprehensive Analysis of Highly Efficient, High-Gain, Modular and Scalable, Dual-Linear-Polarized Metalens Antenna for SatCom Applications

Metalens antennas are emerging as promising candidates for compact high-gain antenna systems in next-generation wireless and satellite communication applications, where stringent link-budget requirements demand highly directive yet lightweight and compact apertures. In such systems, improving aperture efficiency is critical because it reduces the physical aperture size required to achieve a target gain, thereby enabling antenna miniaturization. Although recent studies have explored generative artificial intelligence (AI) techniques for unit-cell optimization, realizing compact and efficient metalens antennas requires a broader metalens-system-level design approach. In this work, a modular, scalable, dual-linearly polarized metalens antenna is proposed through a systematic investigation of the key parameters governing aperture efficiency, thereby enhancing overall antenna compactness. Unlike prior works primarily focused on unit-cell optimization, the proposed approach jointly optimizes both the feed antenna and the metalens structure to achieve efficient aperture illumination and reduced effective aperture requirements. In particular, the study investigates: 1) unit-cell topology; 2) amplitude thresholding; 3) number of metal layers; 4) interlayer pattern variation; 5) unit-cell dimensions; and 6) spatial placement of unit cells based on feed characteristics. This holistic optimization significantly improves aperture efficiency, enabling high-gain performance with a comparatively smaller aperture. To support practical deployment, a modular architecture based on standard printed circuit board (PCB) panels is introduced, enabling scalable and low-cost fabrication with precise alignment achieved using 3-D-printed fixtures. The proposed design is experimentally validated using a $0.7\times 0.7$ m X-band prototype, achieving a maximum measured gain of 36.3 dBi and a high aperture efficiency of 60.2%. These results demonstrate the potential of the proposed approach for compact, high-gain, and cost-effective satellite communication ground-station systems.

Technology and Language Learning: Effective Podcasting and Video-Based Language Teaching Practices in the Age of Generative AI: Eric Friginal and Wing Yee Jenifer Ho: [Book Review]

KirchhoffNet: End-to-End Analog Circuit Acceleration for ODE-Based Neural Networks

This article introduces KirchhoffNet, a novel class of neural network models inspired by the principles of analog electronic circuitry, specifically Kirchhoff’s laws. KirchhoffNet operates as an analog circuit, where the network input is represented by initial node voltages, and the output corresponds to the node voltages at a specific time. The dynamics of the node voltages are governed by learnable parameters on the edges, and the evolution of these voltages follows a system of ordinary differential equations (ODEs). Despite the absence of traditional neural network components such as convolutional layers, KirchhoffNet achieves outstanding performance across a wide range of machine-learning tasks. We further demonstrate that KirchhoffNet is capable of computing diffusion models, making it a promising candidate for accelerating modern generative AI applications. Most notably, KirchhoffNet can be implemented as a high-speed & low-power analog integrated circuit, which introduces a compelling advantage: irrespective of the number of parameters in the network, its on-chip forward calculation can always be completed within a short time. This property makes KirchhoffNet a highly attractive and scalable paradigm for implementing large-scale neural networks, opening new avenues in the realm of analog neural networks for artificial intelligence (AI).

From Integrity to Efficiency Shame: Rethinking Generative AI in Scholarly Publishing

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.